Analysis of Factors Associated With Statin Adherence in a Hierarchical Model Considering Physician,Pharmacy, Patient, and Prescription Characteristics

BACKGROUND: Adherence with maintenance drug therapy such as HMG-CoA reductase inhibitors (statins) is typically analyzed from the perspective of patient characteristics. OBJECTIVES: To determine the effects of physician and pharmacy characteristics in addition to patient characteristics on variation in adherence rates for 4 statin drugs (atorvastatin, pravastatin, rosuvastatin, and simvastatin) for patients who patronized only 1 pharmacy and 1 prescriber of a statin. METHODS: A retrospective cohort study of 6,436 patients who initiated statin therapy was performed from computerized pharmacy records of 2 large national pharmacy chains. Adherence was defined as the number of 30-day refills within 12 months after initiation of statin therapy. Physician, pharmacy, prescription, and patient covariates were considered in a cross-classified hierarchical regression model. RESULTS: The average number of refills dispensed was 4.75 per patient. Patients younger than 50 years had, on average, 13.6% fewer refills per year than did patients older than 70 years (P less than 0.001). Women were 4.4% less adherent than men (P=0.041). Patients residing in southern states were significantly less adherent than were other patients; they had 19.4% fewer refills per year than did patients from western states (P less than 0.001). Each prescription dispensed for comorbid conditions increased adherence by 2.0% (P=0.002), and patients with a history of cardiovascular drug use were 14.1% more adherent than were other patients (P less than 0.001). Patients on a higher statin dose appeared to be 8.4% less adherent than were patients on a lower dose (P less than 0.001). Adherence was greater as the number of prescribed refills increased, with a rate of 2.1% per refill (P less than 0.001). Adherence was lower for patients with higher copayments, with a rate of 2.2% per each additional $10 of copayment (P less than 0.001). For patients treated by physicians in the top 2.5 percentile and bottom 2.5 percentile of statin adherence, mean refill counts per year were 6.1 and 2.9, respectively. For patients who patronized pharmacies in the top 2.5 percentile and bottom 2.5 percentile of statin adherence, mean refill counts per year were 6.6 and 2.5, respectively. Adherence increased at a rate of 28.4% per each additional 100 statin patients per patronized pharmacy (P less than0.001) and decreased at a rate of about 6.5% per each additional 10 statin patients per treating physician (P less than 0.001). CONCLUSIONS: Because of the variability in adherence rates across pharmacies and physicians, further assessment of pharmacy and physician characteristics in addition to patient characteristics may be of value in improving adherence.


Analysis of Factors Associated With Statin Adherence
in aHierarchical Model Considering Physician, Pharmacy, Patient, and Prescription Characteristics alexander Pedan, PhD; Laleh T. Varasteh, RPh, MsF; and sebastian schneeweiss, MD,s cD T herea re many efficacious medications available today to treat and prevent conditions of considerable morbidity and mortality.H owever,i nt he majority of cases, treatment success is suboptimal. The most common reason for treatment failureislack of adherence to the prescribed drug therapy.I ng eneral, fewer than 50% of patients receiving long-term treatment adequately adheret ot heir prescribed regimens, regardless of their disease state. [1][2][3] Al ow level of medication adherence for conditions such as diabetes, hypertension, hypercholesterolemia, and congestive heart failurei sa ssociated with ah igher level of disease-related medical costs. 4 BACKGROUND: Adherence with maintenance drug therapysuchasHMG-CoA reductase inhibitors (statins) is typically analyzed from the perspective of patient characteristics.
OBJECTIVE: To determine the effects of physician and pharmacycharacteristics in addition to patient characteristics on variation in adherence rates for 4statin drugs (atorvastatin, pravastatin, rosuvastatin, and simvastatin) for patients who patronized only 1pharmacyand 1prescriber of astatin.
METHODS: Aretrospective cohort studyof6,436 patients who initiated statin therapywas performed from computerized pharmacyrecords of 2large national pharmacychains. Adherence wasdefined as the number of 30-day refills within 12 months after initiation of statin therapy. Physician, pharmacy, prescription, and patient covariates wereconsidered in across-classified hierarchical regression model. RESULTS: The average number of refills dispensed was4.75 per patient. Patients younger than 50 years had, on average,13.6% fewer refills per year than did patients older than 70 years ( P <0.001). Women were4.4% less adherent than men ( P =0.041). Patients residing in southernstates weresignificantly less adherent than wereother patients; they had 19.4% fewer refills per year than did patients from westernstates ( P <0.001). Each prescription dispensed for comorbid conditions increased adherence by 2.0% ( P =0.002), and patients with ahistoryofcardiovascular drug use were14.1% moreadherent than wereother patients ( P <0.001). Patients on ahigher statin dose appeared to be 8.4% less adherent than werepatients on alower dose ( P <0.001). Adherence wasgreater as the number of prescribed refills increased, with arate of 2.1% per refill ( P <0.001). Adherence waslower for patients with higher copayments, with arate of 2.2% per eachadditional $10 of copayment ( P <0.001). For patients treated by physicians in the top 2.5 percentile and bottom 2.5 percentile of statin adherence,mean refill counts per year were6.1 and 2.9, respectively.For patients who patronized pharmacies in the top 2.5 percentile and bottom 2.5 percentile of statin adherence, mean refill counts per year were6.6 and 2.5, respectively.Adherence increased at arate of 28.4% per eachadditional 100 statin patients per patronized pharmacy(P <0.001) and decreased at arate of about 6.5% per eachadditional 10 statin patients per treating physician ( P <0.001).
CONCLUSION: Because of the variability in adherence rates across pharmacies and physicians, further assessment of pharmacyand physician characteristics in addition to patient characteristics may be of value in improving adherence. •The majority of patients for whom statins are prescribed in routine clinical practice either stop taking the drug altogether or take less than the prescribed dose.
•T odate, most studies of adherence have studied only the effects of patient characteristics on patient adherence. Few studies have examined how much of av ariability in patient adherence outcome is attributed to an individual physician, and no studies have been published on how much is attributed to an individual pharmacy.

What this study adds
•T oo ur knowledge, this study is the first attempt to assess the amount of variation in adherence that can be attributed separately to physicians and pharmacies, after adjusting for patient case mix.
•Adherence was greater as the number of statin patients using a particular pharmacy increased, at ar ate of 28.4% per each additional 100 statin patients per pharmacy.Asignificant inverse relationship was observed between the number of statin patients treated by agiven physician and adherence: adherence decreased at ar ate of about 6.5% per each additional 10 statin patients per physician.
•For patients who patronized pharmacies in the top 2.5 percentile and bottom 2.5 percentile of statin adherence, mean refill counts per year were 6.6 and 2.5, respectively.For patients treated by physicians in the top 2.5 percentile and bottom 2.5 percentile of statina dherence, mean refill counts per year were 6.1 and 2.9, respectively. aBsTRacT

What is already known about this subject
For medications that prevent futuremorbidity,such as lipidlowering and antihypertensive drugs, it has been particularly difficult to achieve high levels of adherence. The efficacy of lipid-lowering therapy in reducing the burden of coronary heart disease (CHD), the leading cause of death in the United States, is well established. 5-9 HMG-CoA reductase inhibitors (statins) can significantly reduce the incidence of CHD and mortality from acute myocardial infarction. However,adherence to statin regimens is critical for the successful prevention of CHD. The majority of patients for whom statins arep rescribed in routine clinical practice either stop taking the drug altogether or take less than the prescribed dose. Cohort studies of patients who wereprescribed statins show variable and often high rates of therapy discontinuation. [10][11][12] reported that only about 50% of patients who werep rescribed al ipid-lowering drug weres till taking it 6m onths later. 13 The percentage of adherent patients dropped to 30% to 40% after 12 months. 13,14 Therea re many barriers to adherence, including lack of education, cost of treatment, low physician trust, side effects, inadequate provider-patient communication, and convenience factors. [15][16][17] No standardstrategy exists for improving adherence. Several comprehensive pharmacy careprograms have been used that have improved patient adherence and outcomes. An ew multiphase study published by Lee and colleagues tested medication adherence in community-based patients aged 65 years and older who received usual carec ompared with continued pharmacy care( standardized medication education, regular follow-up by pharmacists, and medications dispensed in atime-specific manner). This intervention improved medication adherence and persistence and resulted in meaningful clinical reductions in blood pressure; discontinuation of the program resulted in lower medication adherence and persistence. 15 Other clinical studies have also shown ap ositive impact on patient medication adherence. Project ImPACT took place at 26 community-based ambulatoryc arep harmacies. As part of the triangle of care( patient, pharmacist, and physician), pharmacists actively educated patients about the risks associated with high cholesterol levels, the importance of controlling their cholesterol levels, and treatment goals. Pharmacists also conferred with physicians on the type of medication needed for each patient. This study demonstrated that clear communication and collaboration between patients, pharmacists, and physicians could result in improved adherence, enabling patients to reach their treatment goal. 18 Other studies have shown that poor relationships and/or poor communication between patient and physician can lead to low rates of adherence. Piette and colleagues examined the role of patient-physician trust in medication adherence. They found that when physician trust levels werel ow,p atients werem orel ikely to forgo medication treatment. 16 Young and colleagues assessed physician information-giving habits across internists and family physicians prescribing antidepressants. The results showed that physicians provided limited information to patients and frequently did not discuss information critical to improving adherence to therapy. 19 One of the challenges for planning effective interventions to improve medication adherence is to identify the weakest element in the chain from prescribing to persistent use by patients. If such targets for intervention can be identified, then relatively expensive programs developed to improve adherence can become morec ost-effective. Strategies and interventions to improve adherence on the individual patient level are highly variable and include adherence aids, refill or follow-up reminders, regimen simplification, various subsidies such as coupons and rebates, written and oral education, and comprehensive medication and disease management. 20 Aside from patients, physicians and pharmacies arep otential intervention targets. The main objective of our analysis is to assess the relative importanceofpharmaciesandphysiciansonthevariationinpatient adherence to statin therapy.Anadditional goal is to examine the effects of patient-, prescription-, and provider-level characteristics on statin adherence.
nn Methods

Patients
Ar etrospective cohort study was performed for patients who initiated statin therapy.T he data for this study wereo btained from blinded computerized pharmacy prescription records of 2n ational pharmacy chains representing moret han 4,300 community pharmacies nationwide. Data contained prescription drug activity for all prescriptions filled at these national chains for each individual patient, regardless of health careplan. Patients weres elected on the basis of the presence of an initial prescription for atorvastatin, rosuvastatin, pravastatin, or

Analysis of Factors Associated With Statin Adherence in aHierarchical Model
Considering Physician, Pharmacy,Patient, and Prescription Characteristics simvastatin between August 1, 2004, andSeptember 30, 2004 (Figure1). Other statins (i.e., fluvastatin, generic lovastatin, and simvastatin with ezetimibe) constituted less than 3% of all statin prescriptions and weren ot included in the analysis because of the low volume. The date of the first statin dispensing during the study period was the index date of the analysis. Only patients who had no statin dispensed in the 6m onths beforet he index date in the same pharmacy chain wereincluded in the study ( Figure  2). The analysis was restricted to include index prescriptions with only a3 0-day supply to avoid misclassifying adherence by ensuring that all study patients had an equivalent starting point. (Patients with less than a3 0-day supply for the index script would have, on average, morerefills than would patients with a3 0-day supply,m aking these patients erroneously look morea dherent. Conversely,p atients with moret han a3 0-day supply for the index script would have, on average, fewer refills than would patients with a30-day supply,making these patients erroneously look less adherent.) Patients who filled statin prescriptions written by more than 1p hysician or who patronized moret han 1p harmacy within ac hain weree xcluded from the analysis. Patient adherence was evaluated for 1year after the index date. The analysis was further restricted to physicians and pharmacies who represented at least 4patients eligible for our analysis during the study period to allow for morestable multilevel estimates.
All patient identifiers wered eleted after linkage, and nontraceable study ID numbers werea ssigned. This study was approved by the Institutional Review Boardo fB righam and Women' sHospital.

Adherence Outcome
Rates of refilling prescriptions areoften used as an accurate and objective measureo fo verall prescription drug adherence. 21,22 Since statin therapy is chronic in nature, we assumed that any completed prescription (i.e., new prescription plus all authorized refills) was going to be followed by an ew prescription. The outcome variable was defined as the total number of 30-day refills that patients obtained during the 1-year followup period.
All analyses werep erformed at the patient level; all statin fills for each patient weresummed even if they represented new prescription numbers or switches from one statin to another. Few patients (approximately 4%), however,switched to another statin during the study evaluation period. About 4.1% of all patients selected for the analysis received the first prescription with a30-day supply and then the following prescription(s) with a60-or 90-day supply.For these patients, we created ap roxy of 30-day refills, counting, for example, arefill with a60-or 90-day supply as 2or3refills with a30-day supply.Patients with 11 or morerefills wereconsidered fully adherent.

Covariates
The measured patient-level characteristics included age, gender, number of existing comorbid conditions (measured using a count of Chronic Disease Score[ CDS] disease categories 23 ), historyo fc ardiovascular diseases (measured using the cardiovascular component of the CDS), region of residence (Northeast, Midwest, South, and West), and index prescription-specific characteristics (daily dose, number of refills prescribed, and copayment). Patients' baseline comorbid conditions and history of cardiovascular diseases werei dentified from prescriptions filled during the 6-month period beforet he index date. The daily doses wereg rouped into high and low categories, where the low-dose categoryi ncluded patients with the prescribed daily doses of 10 mg or less for atorvastatin and simvastatin, 5mgorless for rosuvastatin, and 20 mg or less for pravastatin. Physician-and pharmacy-level characteristics consisted of the total number of patients treated with lipid-lowering drugs in the enrollment period and pharmacy chain indicators.

Statistical Analysis Using Hierarchical Models
Patient, physician, and pharmacy characteristics area ll important factors affecting patient-level adherence outcomes.
Traditional multivariate techniques treat observations as though they werei ndependent. However,p atients in ag iven physician practice or pharmacy may sharec haracteristics, and their outcomes areu nlikely to be truly independent of one another. For example, ap hysician may tailor his/her clinical practice towards pecific diseases and/or socioeconomic subgroups. At the pharmacy level, the pharmacy location, size, daily prescription volume, number of pharmacists, degree of counseling, etc., may result in less heterogeneity in the patient population. Patients associated with ap hysician or with ap harmacy form an atural 2-level hierarchical structure. Ignoring the clustering that exists in hierarchical data may result in abiased estimation of both parameter estimates and their variances, and thereforewill result in afalse statistical inference. Astatistical technique that is well suited to explorethe effects of pharmacy and physician characteristics on patient adherence is multilevel modeling or random effects regression models. [24][25][26][27] Additional hierarchical levels can be easily added to the model. The 2-level hierarchy of physicians and patients (1 physician sees several patients) could be expanded to include pharmacies at at hirdl evel. However,n oc lear hierarchy can be defined between physicians and pharmacies because patients can fill prescriptions at different pharmacies. Conversely,customers of a specific pharmacy aret reated by many different physicians. In this situation, patients ares aid to be contained within crossclassification of physicians by pharmacies. 26 We will denote by Y i (j 1 ,j 2 )t he adherence outcome for patient i, treated by physician j 1 ,a nd filling prescriptions at pharmacy j 2 ,a nd the corresponding expected value is denoted as µ i ( j 1,j 2) .The adherence outcome is the count of 30-day refills in 12 months. The standardd istribution for counts is the Poisson distribution. However,inreal-life applications, variability among counts is usually greater than would be expected by simple Poisson distribution. Such extra variability is called overdispersion. The negative binomial regression is the generalization of the Poisson regression, which takes into account the possibility of overdispersion. To account for possible overdispersion, the adherence outcome was modeled with an egative binomial distribution. 28,29 One of the important features of negative binomial distributions is that the variance is not af reep arameter as in the case of anormal distribution, but is afunction of the mean. In terms of hierarchical modeling, this leads to ar elationship between the parameters in the fixed part of the model and the parameters of the random part. 26 To answer the question of how much variability in the patient' sa dherence outcome is attributable to the process occurring at the pharmacy level than at the physician level, we will consider the following cross-classified negative binomial hierarchical model: where( X β) i ( j 1 , j 2 ) is the set of linear predictors and parameters u j 1 and u j 2 represent the physician-specific and pharmacy-specific contributions to patient adherence.
Herewemodel the negative binomial variation at the patient level and assume that variations at physician and pharmacy levels arei ndependent and normally distributed with means of 0 and variances of σ 2 u 1 and σ 2 u 2 ,respectively.The log-linear natureof this regression means that the independent predictors ( X β) i ( j 1 , j 2 ) and the level-2 random parameters u j 1 and u j 2 have multiplicative effects on the expected counts of refills. For example, if thereisonly 1e xplanatoryv ariable X 1 ,t he right side of the above equation is equivalent to exp(β 0 )exp(β 1 X 1 )exp ( u j 1 )exp(u j 2 ), where β 0 is the intercept. Therefore, each additional unit of X 1 will have the effect of multiplying the expected number of refills by exp(β 1 ).
Similarly,i nap harmacy with ah igh intercept, for example, 2standarddeviations, so that u j 2 =2 σ u 2 ,the expected number of refills will be exp(2σ u 2 )times as high as in apharmacy with the average value, u j 2 =0,o ft he intercept. 25 Another consequence of multiplicity of effects is that no simple relationship exists between the variance of an outcome and the variances of the random effects. 26 The significance of the fixed parameters is evaluated using 2-sided t tests. The interactions between different covariates werec hecked and found to be nonsignificant at the α =0.1 level. Accordingly,the fnal multivariate model included only main effects. The significance of the random parameters is evaluated by the Wald test, comparing the estimates divided by their standarde rrors with as tandardn ormal distribution. Because the variance components areb ounded by 0, the null hypotheses of no variance (H 0 σ 2 uk :=0,k=1 ,2)are tested against the 1-sided alternative hypotheses (H 1 : σ 2 uk >0, k=1, 2). Parameter estimation in the above hierarchical generalized linear model is done by restricted pseudo-likelihood methods as implemented in the SAS GLIMMIX procedure( SAS v. 9.13, Cary, NC). 30 nn

Results
The final cohort consisted of 6,436 patients initiating statin therapy at 586 pharmacies prescribed by 1,059 physicians. Table 1l ists baseline patient and prescription characteristics. The average age of the patients was 59.9 years (SD=13.1, range 8-101) and 49.8% werefemale. The mean statin copayment for the index prescription was $28.64 (SD =$32.67, range $0-$377.50). Patients had, on average, 2.0 (SD=2.0, range 0-12) prescriptions dispensed for comorbid conditions (defined by CDS categories); moret han 27% of patients had no prescrip-tions for other conditions. Almost half of all patients (43.9%) had ap rior historyo fc ardiovascular medication use ( Table 1). The mean number of statin patients per physician was 9.8 (SD =7.0, range 4-46) and the mean number of statin patients per pharmacy was 63.3 (SD=35.4, range 10-264).
Figure3d isplays the observed frequency of refills picked up. The distribution is highly right-skewed, with 18.5% of all patients not picking up any refills. The average number of refills dispensed was 4.7 (SD =4.0, range 0-13) per patient. The variance is considerably greater than the mean, indicating that an egative binomial distribution is an adequate assumption for distribution of refills. The large variance of the outcome signifies that ar elatively big sample size is required to estimate the effects of various prognostic factors of adherence with enough precision. Table 2s hows results of the cross-classified multivariate hierarchical regression model of patient adherence to statin therapy.B ased on the significance level, adherence was most strongly associated with the number of refills prescribed and copayment for the index prescription. Patients and index prescription characteristics that aresignificantly associated with adherence included age, gender,r egion of residence, index copayment, index dose, number of prescriptions dispensed for comorbid conditions, and prior historyo fc ardiovascular disease. As discussed earlier,t he effect of each predictor on patient adherence can be quantified by exponentiation of parameter estimates from Table 2, exp(β ). On the basis of these calculations, we can conclude that patients younger than 50 years had, on average, 13.6% fewer refills per year (exp(β )=0.864) than did patients older than 70 years ( P <0.001). Women were4 .4% less adherent than werem en ( P =0.041). Patients residing in southerns tates had 19.4% fewer refills per year than did patients from westerns tates ( P <0.001). Each comorbid condition increased adherence by 2.0% ( P =0.002) and patients with ah istoryo fc ardiovascular drug use were 14.1% morea dherent than wereo ther patients ( P <0.001). Patients on ah igher statin dose appeared to be 8.4% less adherent than werep atients on al ower statin dose ( P <0.001). Adherence was greater as the number of prescribed refills increased, with ar ate of 2.1% per refill prescribed ( P <0.001). Adherence was lower for patients with ahigher copayment, at a rate of 2.2% per each additional $10 of copayment ( P <0.001).
At the second level of our hierarchical structure, adherence was greater,a st he number of statin patients using ap articular pharmacy increased, with ar ate of 28.4% per each additional 100 statin patients per pharmacy ( P <0.001). Also, asignificant inverse relationship was observed between the number of statin patients treated by agiven physician and adherence: adherence decreased at arate of about 6.5% per each additional 10 statin patients per physician ( P <0.001).
The physician-( σ 2 u 1 )a nd pharmacy-level ( σ 2 u 2 )v ariance components (and their standarde rrors) yield the following estimates of 0.021 (0.007) and 0.035 (0.007), respectively, indicating slightly morevariability across pharmacies than across physicians with respect to patient adherence to statin therapy. The variances at both physician and pharmacy levels arehighly statistically significant. As mentioned earlier,r andom effect  parameters have amultiplicative effect on the expected counts of refills. Particularly,each expected count of refills is multiplied by exp(u j 1 )toaccount for the effect of specific physician and by exp ( u j 2 )t oa ccount for the effect of specific pharmacy (with these multipliers equal to 1f or average values of u j 1 =0 and u j 2 =0 For patients who patronized pharmacies in the top 2.5 percentile and bottom 2.5 percentile of statin adherence, mean refill counts per year were6 .6 and 2.5, respectively.F or patients treated by physicians in the top 2.5 percentile and bottom 2.5 percentile of statin adherence, mean refill counts per year were6.1 and 2.9, respectively.

nn Discussion
The study used ac ross-classified hierarchical model to analyze patient adherence to statin therapy.T his model estimated the relative variation in patient adherence by physician and pharmacy (random effects) after adjusting for patient, index prescription, and health carep rovider characteristics (fixed effects). This model was used to avoid key weaknesses of conventional (nonhierarchical) regression models, which neglect clustering of patients within pharmacies and physicians. To our knowledge, this study is the first attempt to assess the degree of variation in adherence that can be attributed separately to physicians and pharmacies, after adjusting for patient case mix.
We found that the percentage of patients on statin therapy dropped sharply after the index fill, with approximately 18% of all statin initiators not having as econd fill after the index prescription. Ah igher number of refills prescribed and lower copayment amount for the index prescription werea ssociated with better adherence to statin therapy.T he latter result is consistent with conclusions of recently published studies that analyzed the effects of prescription drug copayments on statin adherence. 12,31 Ah igher number of comorbid conditions, particularly those treated with cardiovascular drugs, was associated with ahigher rate of refills, similar to the findings of other studies. 32,33 Our study also found high variation in adherence to statins across pharmacies and physicians that was not explained by patient case mix. The variation in adherence was larger among pharmacies than among physicians.

Analysis of Factors Associated With Statin Adherence in aHierarchical Model
Considering Physician, Pharmacy,Patient, and Prescription Characteristics We observed that patients who filled their statin prescriptions at pharmacies with ah igh volume of statin prescriptions showed, on average, better adherence. We could not determine whether this observation was because of well-trained pharmacists in the area of cardiovascular disease at these pharmacies, disease management programs, or other factors unmeasured by our analysis.
We also found that, on average, patients treated by physicians who prescribed morestatin prescriptions had alower adherence rate than did those treated by physicians who prescribed fewer statin prescriptions. We did not capturet he medical specialty of the physicians in this study and thereforec annot speculate on the possible influence of medical specialty. However,h igh prescribers may have less time per patient to engage in counseling that may influence adherence to statin therapy.P revious research has shown that general practitioners have morep atients using lipid-lowering medications than do specialists, 34 and general practitioners initiate about 80% of prescriptions for statins. 35 All physicians report time pressures, and primaryc arep hysicians may have less time per patient. 36 We focused on pharmacy and physician factors in this study,but patient characteristics have been associated with adherence to lipid-modifying therapy.W ek now from previous research that patients at higher risk for coronaryarterydisease aremorelikely to adheret ot heir treatment than arep atients who take it for primaryprevention. 37 The factors that affect clinical outcomes in lipid-modifying therapy include educating patients, monitoring their response to therapy,a nd having interventions targeted at behaviors of patients and prescribers. 38 In addition, one of the major barriers to adherence is poor communication between the physician and the patient. According to Marinker et al., patients and physicians should form atherapeutic alliance to "optimize health gain from the best use of medicines, compatible with what the patient desires and is capable of achieving." 39 This is referred to as concordance. To facilitate full concordance, special training in communication may be necessaryfor health careproviders. 40 Another opportunity for the health carep rovider is to identify patients with high risk for nonadherence and to be more aggressive in efforts to monitor and communicate with their patients on an individual basis. Reminding physicians about communicating with their patients regarding the importance of adherence to therapy and providing physicians with al ist of their nonadherent patients can positively affect patient adherence from the perspective of population disease management. 41 Improving patient adherence may be achieved through pharmacy-based programs, whereac ombination of patient education and provider awareness is available. 42 Since medications arei mportant in the treatment of chronic conditions and because pharmacists have significant knowledge of medications, they play acritical role in disease management. 43,44 Pharmacists arethe most accessible health careproviders to the patient once medication therapy is initiated. 38 Therefore, pharmacy care models can promote behavioral changes among patients and should be an important and integral part of the overall treatment plan.
The Asheville Project assessed the clinical, humanistic, and economic outcomes of ac ommunity-based medication therapy management (MTM) program. The study found that patients with asthma or diabetes who received ongoing education and long-term MTM achieved and maintained improvements in their condition and had significantly lower disease-related costs. 43,44 Assessing adherence by pharmacy characteristics may be of value when it comes to improving adherence with prescribed therapy,particularly for health plans with acommitment to health maintenance. Hierarchical models, such as those employed in this study,c an be used to assess unusual performance of specific physicians or pharmacies that represent patients with particularly poor adherence. These providers can be targeted by customized interventions to improve adherence. Of course, in actual practice, statistical analysis can provide only ap reliminaryi ndication of suboptimal performance, and more detailed investigation is necessaryt ov erify targets of opportunity for clinical practice improvement.

Limitations
This study relied on the dispensing records of 2p harmacy chains. While this method in some ways permits mored etailed examination of individual patients within ap harmacy chain, it also means that, in our analysis, ap atient is lost to follow-up when he or she obtains ar efill or new prescription at ap harmacy in ad ifferent chain. So the first limitation is that patients who switched pharmacy chains werec onsidered discontinued in our analysis.
Second, exclusion of the only generic statin (lovastatin) creates alimitation of this study,particularly with respect to the relationship between copayment amount and adherence. Our study design, which required a6 -month preindex wash-out period without statin use, resulted in low counts of new generic lovastatin users. Since generic lovastatin has significantly lower cost than the brand statins, exclusion of this drug could potentially have some additional effect on the established relationship between adherence and copayment. However,morethan 36% of all patients in our sample paid less than a$ 10 copayment for the index script. This number is big enough to reliably assess the influence of low copayments on patient adherence.
Third, our analysis included only statin patients with index prescriptions for a3 0-day supply,w hich resulted in the loss of about 23% of the initial patient population. This selection was made to make interpretation of the results morec onsistent; however,i tl imits the degree to which our results can be generalized to all statin patients.
Fourth, while pharmacy data arev erya ccurate in recording actual dispensing and prescription pick-up by patients, these data lack information on whether ap articular patient actually consumes the medication. However,s tudies have shown that dispensing data areag ood marker for actual use. 21 Assuming some nonuse of dispensed drugs, we realize that the reported adherence rates in the present study overestimate actual adherence.
Fifth, although the 2s elected pharmacy chains aren ationwide and reasonably representative of the population of pharmacies, patient and pharmacy characteristics within these chains may not be representative.
Sixth, even though this analysis is adjusted for patient and health carep rovider characteristics, it is likely that not all characteristics that arer elevant predictors of adherence were captured. For example, unmeasured patient factors such as severity of disease, patient race, English proficiency,p atient income, and educational attainment may be clustered in pharmacies or physician practices and thereforemay cause some residual confounding in our analysis. At the health carep rovider level, we didn'th ave information on physician gender,a ge, years in practice, degree, medical specialty and boardt raining, physi-

Analysis of Factors Associated With Statin Adherence in aHierarchical Model
Considering Physician, Pharmacy,Patient, and Prescription Characteristics cian/pharmacist attitudes on the importance of counseling, the number of staffi np hysician offices and in pharmacies, and whether some pharmacies operated medication adherence programs.
Seventh, the present model assumes that patients areassociated with as ingle physician or pharmacy,w hich may not accurately reflect real-life situations. Instead, patients can be treated by several physicians, or they can fill prescriptions at several pharmacies. Such further complexity can be taken into account by the use of so-called multiple membership models, 26 which arev eryo ften computationally intractable. However, statin patients using morethan 1physician or pharmacy constituted less then 16% of our original statin patient population (Figure2), which, along with the large patient sample size and a large number of health carep roviders in this analysis, should make our results sufficiently robust.
nn Conclusions Lack of adherence with prescribed therapy is awell-documented problem that can greatly affect patients' health outcomes. Although the current literaturer ecognizes this problem as multifaceted, it mostly covers the role of the patients and their behavior in adhering to therapy.O ur study not only examined the impact of patient characteristics on medication adherence but also examined the possible influence of the pharmacy and the physician in this critical aspect of the treatment.
Our hierarchical modeling approach revealed that therea re large variations in patient adherence to statin therapy among both pharmacies and physicians, which translates into al arge difference in the refill rates between the best-and worstperforming health carep roviders at the extreme ends of the distributions. It also showed that high-volume physician practices that prescribed morestatins wereassociated with lower patient adherence. In contrast, pharmacies that dispensed more statins werea ssociated with greater patient adherence to statin therapy.

Disclosures
No outside funding supported this research. Authors Alexander Pedan and Laleh T. Varasteh disclose that they have been funded by the internal resources of Adheris, Inc., avendor of communication materials and interventions designed to increase patient adherence to drug therapy.Author Sebastian Schneeweiss is principal investigator of the Brigham and Women' sHospital DEcIDE Research Center,funded by the Agency for HealthcareResearch and Quality.He is also funded by grants from the Agency for HealthcareResearch and Quality and the National Institute on Aging.
Pedan served as principal author of the study.Study concept and design werecontributed primarily by Pedan, with input from the coauthors. Data collection was the work of Pedan, with input from Varasteh; data interpretation was the work of Pedan and Varasteh. Writing of the manuscript and its revision werethe work of Pedan and Varasteh.